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Michael Zink

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2 papers

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AAMAS Conference 2011 Conference Paper

Agent-Mediated Multi-Step Optimization for Resource Allocation in Distributed Sensor Networks

Bo An
Victor Lesser
David Westbrook
Michael Zink

Distributed collaborative adaptive sensing (DCAS) of the atmosphere is a new paradigm for detecting and predicting hazardous weather using a large dense network of short-range, low-powered radars to sense the lowest few kilometers of the earths atmosphere. In DCAS, radars are controlled by a collection of Meteorological Command and Control (MC&C) agents that instruct where to scan based on emerging weather conditions. Within this context, this work concentrates on designing efficient approaches for allocating sensing resources to cope with restricted real-time requirements and limited computational resources. We have developed a new approach based on explicit goals that can span multiple system heartbeats. This allows us to reason ahead about sensor allocations based on expected requirements of goals as they project forward in time. Each goal explicitly specifies end-users' preferences as well as a prediction of how a phenomena will move. We use a genetic algorithm to generate scanning strategies of each single MC&C and a distributed negotiation model to coordinate multiple MC&Cs' scanning strategies over multiple heartbeats. Simulation results show that as compared to simpler variants of our approach, the proposed distributed model achieved the highest social welfare. Our approach also has exhibited similarly very good performance in an operational radar testbed that is deployed in Oklahoma to observe severe weather events.

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AAMAS Conference 2010 Conference Paper

Automated Negotiation with Decommitment for Dynamic Resource Allocation in Cloud Computing

Bo An
Victor Lesser
David Irwin
Michael Zink

We consider the problem of allocating networked resources in dynamic environment, such as cloud computing platforms, where providers strategically price resources to maximize their utility. Resource allocation in these environments, where both providers and consumers are selfish agents, presents numerous challenges since the number of consumers and their resource demand is highly dynamic. While numerous auction-based approaches have been proposed in the literature, this paper explores an alternative approach where providers and consumers automatically negotiate resource leasing contracts. Since resource demand and supply can be dynamic and uncertain, we propose a distributed negotiation mechanism where agents negotiate over both a contract price and a decommitment penalty, which allows agents to decommit from contracts at a cost. We compare our approach experimentally, using representative scenarios and workloads, to both combinatorial auctions and the fixed-price model used by Amazon's Elastic Compute Cloud, and show that the negotiation model achieves a higher social welfare.

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